Research and Innovation

Helping authorities track and manage CO2 levels across the city in real time, as well as cumulatively over hours, days, or weeks Helping office-goers plan their commute, taking traffic congestion, transport time tables, and commuter loads into consideration Ensuring the safety of independent seniors by tracking their movement at home during waking hours and alerting caregivers and emergency services in case of no movement Enabling automated detection and alerting of emergency services such as the fire department or ambulances in case of fires

These are a few instances of connected and intelligent services that come to mind when we envision a ‘smart city’. No longer existing only in the realm of possibilities, several cities around the world have already made strides in adopting intelligent digitization of governance, healthcare, public transportation, connectivity, and sustainable energy sources.

A key feature of enabling these smart services, including the delivery of personalized citizen engagement like in the scenarios listed above, is the flow of standardized data across applications, sensors, and other third-party services. This is critical to ensure a collaborative and scalable environment that can deliver the right information in the right context, across stakeholders.

Architecturally, we refer to the layer that facilitates such data flow as the ‘intelligent data hub’, and believe that it is the core piece enabling smart city administrators to develop sustainable intelligent capabilities. Let’s look at this layer in more detail.

The Intelligent Data Hub

All cities are different, with their own cultural and social norms and underlying administrative infrastructure. The smart architecture that is developed on top of it therefore differs across all smart cities, too.

However, across smart cities, when we look at the various layers of IT solution architecture, we find a set of well-defined interrelated components abstracting the state and behavior of city. Although formally referred to by different names, this layer represents the intelligent data hub, which is the single source of truth, abstracting, storing, and sharing data from multiple sources in a structured format after enrichment with contextual knowledge.

An intelligent data hub should ideally be able to store data from:

A variety of sensors and devices across domains such as traffic, utilities, mobility, and the environment Interpreted or processed crowdsourced information based on citizen updates on social media; for example, tweets about traffic congestions Enterprise and silo solutions for specific contextual insights; for example, traffic density on Wednesday, June 12, 2017, 8.30 AM at Colaba, Mumbai At the center of such data abstraction and storage is a unified and standardized data platform, which is especially important considering the staggered onboarding of smart city capabilities. This helps restrict siloed data structures, which can lead to a technologically complex, non-collaborative, and difficult-to-scale environment. Taking a siloed approach also challenges the usability and sustainability of smart services.

Scope of Capabilities

Smart cities today use such data hubs as a horizontal layer, thus enabling an interoperability standard across multiple silos for extended collaboration. This hub could be custom-built for a city, or based on a concept model like BSI PAS 182, which is a framework to normalize and classify information from many sources.

PAS 182 enables data sets to be discovered and combined in a way that gives a clearer picture of the needs and behaviors of a city’s citizens (residents and businesses). In case of an existing capability, the data hub will not replace existing models, but allow mapping between the data hub and the parent modules, enabling the sharing of data in a unified format.

Following are some of the smart city capabilities enabled by intelligent data hubs:

Asking a single platform to deliver a variety of contextualized and correlated insights leveraging: Raw data (real-time, near real-time, and historic); for example, CO2 level currently, over the past three minutes, one hour, or one week Orchestrated data; for example, traffic density when CO2 levels are high Business and context-enriched data; for example, the time at which Mr. X should leave home to reach office by 10.45 AM Processing complex data sets and deriving actionable insights; for example, monitoring of independent senior citizens, or detecting and reacting to fires Opening up the city data for innovations led by city stakeholders, including external third parties Driving innovation in services over the unified environment, enabling more citizen-centric and need-based delivery Facilitating data virtualization, where the data hub becomes a pass-through layer, by exposing a catalog of services (APIs), which are discoverable and provisioned based on subscriptions. Frameworks such as RDF can be leveraged for exposing APIs. Structured and standardized mechanisms like these can help smart city applications interface with the unified data hub, and use the data to transform existing/new applications to intelligent ones Thus, in the case of smart cities, an intelligent data hub can play a pivotal role in realizing collaborative and contextual use cases. I leave you with a high-level view of the intelligent data hub within the overall smart city architecture.

What are some of the other smart city use cases that you have seen realized with the help of a data hub?

Helping authorities track and manage CO2 levels across the city in real time, as well as cumulatively over hours, days, or weeks

Ensuring the safety of independent seniors by tracking their movement at home during waking hours and alerting caregivers and emergency services in case of no movement

Enabling automated detection and alerting of emergency services such as the fire department or ambulances in case of fires

These are a few instances of connected and intelligent services that come to mind when we envision a ‘smart city’. No longer existing only in the realm of possibilities, several cities around the world have already made strides in adopting intelligent digitization of governance, healthcare, public transportation, connectivity, and sustainable energy sources.

A key feature of enabling these smart services, including the delivery of personalized citizen engagement like in the scenarios listed above, is the flow of standardized data across applications, sensors, and other third-party services. This is critical to ensure a collaborative and scalable environment that can deliver the right information in the right context, across stakeholders.

Architecturally, we refer to the layer that facilitates such data flow as the ‘intelligent data hub’, and believe that it is the core piece enabling smart city administrators to develop sustainable intelligent capabilities. Let’s look at this layer in more detail.

The Intelligent Data Hub

All cities are different, with their own cultural and social norms and underlying administrative infrastructure. The smart architecture that is developed on top of it therefore differs across all smart cities, too.

However, across smart cities, when we look at the various layers of IT solution architecture, we find a set of well-defined interrelated components abstracting the state and behavior of city. Although formally referred to by different names, this layer represents the intelligent data hub, which is the single source of truth, abstracting, storing, and sharing data from multiple sources in a structured format after enrichment with contextual knowledge.

An intelligent data hub should ideally be able to store data from:

A variety of sensors and devices across domains such as traffic, utilities, mobility, and the environment

Interpreted or processed crowdsourced information based on citizen updates on social media; for example, tweets about traffic congestions

At the center of such data abstraction and storage is a unified and standardized data platform, which is especially important considering the staggered onboarding of smart city capabilities. This helps restrict siloed data structures, which can lead to a technologically complex, non-collaborative, and difficult-to-scale environment. Taking a siloed approach also challenges the usability and sustainability of smart services.

Scope of Capabilities

Smart cities today use such data hubs as a horizontal layer, thus enabling an interoperability standard across multiple silos for extended collaboration. This hub could be custom-built for a city, or based on a concept model like BSI PAS 182, which is a framework to normalize and classify information from many sources.

PAS 182 enables data sets to be discovered and combined in a way that gives a clearer picture of the needs and behaviors of a city’s citizens (residents and businesses). In case of an existing capability, the data hub will not replace existing models, but allow mapping between the data hub and the parent modules, enabling the sharing of data in a unified format.

Following are some of the smart city capabilities enabled by intelligent data hubs:

Asking a single platform to deliver a variety of contextualized and correlated insights leveraging:

Raw data (real-time, near real-time, and historic); for example, CO2 level currently, over the past three minutes, one hour, or one week

Orchestrated data; for example, traffic density when CO2 levels are high

Business and context-enriched data; for example, the time at which Mr. X should leave home to reach office by 10.45 AM

Opening up the city data for innovations led by city stakeholders, including external third parties

Driving innovation in services over the unified environment, enabling more citizen-centric and need-based delivery

Facilitating data virtualization, where the data hub becomes a pass-through layer, by exposing a catalog of services (APIs), which are discoverable and provisioned based on subscriptions. Frameworks such as RDF can be leveraged for exposing APIs. Structured and standardized mechanisms like these can help smart city applications interface with the unified data hub, and use the data to transform existing/new applications to intelligent ones

Thus, in the case of smart cities, an intelligent data hub can play a pivotal role in realizing collaborative and contextual use cases. I leave you with a high-level view of the intelligent data hub within the overall smart city architecture.

What are some of the other smart city use cases that you have seen realized with the help of a data hub?

Helping authorities track and manage CO2 levels across the city in real time, as well as cumulatively over hours, days, or weeks

Ensuring the safety of independent seniors by tracking their movement at home during waking hours and alerting caregivers and emergency services in case of no movement

Enabling automated detection and alerting of emergency services such as the fire department or ambulances in case of fires

These are a few instances of connected and intelligent services that come to mind when we envision a ‘smart city’. No longer existing only in the realm of possibilities, several cities around the world have already made strides in adopting intelligent digitization of governance, healthcare, public transportation, connectivity, and sustainable energy sources.

A key feature of enabling these smart services, including the delivery of personalized citizen engagement like in the scenarios listed above, is the flow of standardized data across applications, sensors, and other third-party services. This is critical to ensure a collaborative and scalable environment that can deliver the right information in the right context, across stakeholders.

Architecturally, we refer to the layer that facilitates such data flow as the ‘intelligent data hub’, and believe that it is the core piece enabling smart city administrators to develop sustainable intelligent capabilities. Let’s look at this layer in more detail.

The Intelligent Data Hub

All cities are different, with their own cultural and social norms and underlying administrative infrastructure. The smart architecture that is developed on top of it therefore differs across all smart cities, too.

However, across smart cities, when we look at the various layers of IT solution architecture, we find a set of well-defined interrelated components abstracting the state and behavior of city. Although formally referred to by different names, this layer represents the intelligent data hub, which is the single source of truth, abstracting, storing, and sharing data from multiple sources in a structured format after enrichment with contextual knowledge.

An intelligent data hub should ideally be able to store data from:

A variety of sensors and devices across domains such as traffic, utilities, mobility, and the environment

Interpreted or processed crowdsourced information based on citizen updates on social media; for example, tweets about traffic congestions

At the center of such data abstraction and storage is a unified and standardized data platform, which is especially important considering the staggered onboarding of smart city capabilities. This helps restrict siloed data structures, which can lead to a technologically complex, non-collaborative, and difficult-to-scale environment. Taking a siloed approach also challenges the usability and sustainability of smart services.

Scope of Capabilities

Smart cities today use such data hubs as a horizontal layer, thus enabling an interoperability standard across multiple silos for extended collaboration. This hub could be custom-built for a city, or based on a concept model like BSI PAS 182, which is a framework to normalize and classify information from many sources.

PAS 182 enables data sets to be discovered and combined in a way that gives a clearer picture of the needs and behaviors of a city’s citizens (residents and businesses). In case of an existing capability, the data hub will not replace existing models, but allow mapping between the data hub and the parent modules, enabling the sharing of data in a unified format.

Following are some of the smart city capabilities enabled by intelligent data hubs:

Asking a single platform to deliver a variety of contextualized and correlated insights leveraging:

Raw data (real-time, near real-time, and historic); for example, CO2 level currently, over the past three minutes, one hour, or one week

Orchestrated data; for example, traffic density when CO2 levels are high

Business and context-enriched data; for example, the time at which Mr. X should leave home to reach office by 10.45 AM

Opening up the city data for innovations led by city stakeholders, including external third parties

Driving innovation in services over the unified environment, enabling more citizen-centric and need-based delivery

Facilitating data virtualization, where the data hub becomes a pass-through layer, by exposing a catalog of services (APIs), which are discoverable and provisioned based on subscriptions. Frameworks such as RDF can be leveraged for exposing APIs. Structured and standardized mechanisms like these can help smart city applications interface with the unified data hub, and use the data to transform existing/new applications to intelligent ones

Thus, in the case of smart cities, an intelligent data hub can play a pivotal role in realizing collaborative and contextual use cases. I leave you with a high-level view of the intelligent data hub within the overall smart city architecture.

What are some of the other smart city use cases that you have seen realized with the help of a data hub?

Arun Vijayakumar is an enterprise architect with TCS Research and Innovation. With over 20 years of IT experience in enterprise architecture and smart city consulting, he presently works on digital citizen and connected social systems research programs within TCS Research and Innovation. As an enterprise architect, he handles the architecture of various solutions and research projects within these programs. Arun holds a Master’s degree in Applied Statistics from the University Campus, University of Kerala, with computer application and operations research as his specializations, and an MBA in operations management from the University of Madras.